MVP Launch Checklist: Everything You Need Before You Ship

You've built the thing. The demo works on your machine. Now the only question that matters: is it safe to hit deploy? An MVP pre-launch checklist isn't a wish list of features — it's a short set of go/no-go gates that must each be green before a stranger touches your product. This page owns that single moment: the final readiness review before you ship.

Most launch disasters don't come from a missing feature. They come from skipping a boring gate — no error tracking, no cost cap, no tested rollback. So treat every item below as binary: it's green, or you don't ship.

What is a pre-launch readiness check?

A pre-launch readiness check is the final go/no-go review where you confirm each launch-critical gate works for a real outsider — not "mostly works" for you. It's the gap between "the feature exists" and "it works for a stranger on a cold device, on mobile data, with an expired card."

For an AI MVP, the readiness check has a specific twist: most of your risk lives in what happens when the model is slow, wrong, or down. A normal web app fails loudly. An AI app fails plausibly — it returns a confident wrong answer, or quietly burns hundreds of dollars in tokens overnight. Your gates have to catch that.

This article stays narrow on purpose: it answers one question, "is it safe to hit deploy?" It is not a growth or marketing plan, and it is not a step-by-step build guide.

The six gates that must be green before you ship

Here are the gates, in priority order. Each is a yes/no question, not a slider.

1. Authentication actually works for a stranger

Sign-up and login are the front door. Test the full loop in an incognito window, on your phone, on mobile data:

• New email signs up, gets a verification email (check spam), and lands logged in.
• Password reset works end to end. (You will get this request on day one.)
• Logging out and back in keeps the user's data.
• OAuth ("Sign in with Google") doesn't dead-end on the callback.

If you're using Supabase or Clerk for auth, confirm your production keys and redirect URLs are set — not the localhost ones. This is the single most common "works on my machine" failure.

2. Billing or waitlist captures the user

Decide your money model before launch, then test the real path:

• Charging now? Run a live test transaction (Stripe test mode, then one real card). Confirm the webhook fires, the user gets access, and a failed card shows a clear error — not a white screen.
• Free for now? Make sure the waitlist or sign-up actually stores the lead somewhere you'll see it. A waitlist form that posts into the void is the most expensive bug in early-stage startups.

Don't build dunning, proration, and invoicing for launch. Capture money or capture intent — that's the gate.

3. AI guardrails: rate limits, fallback, and a hard cost cap

This is the gate generic checklists skip, and it's where AI MVPs actually break. Three sub-gates, all required:

1. Rate limiting per user. Without it, one user (or one bot) hammering your endpoint can rack up cost and crater latency for everyone. Cap requests per minute per user.
2. A fallback for model failure. When GPT-4 or Claude times out, returns a 429, or throws a malformed response, what does the user see? Not a stack trace. Catch the error, show a graceful "try again" state, and ideally retry once or fall back to a cheaper/faster model.
3. A hard spending cap. Set a usage limit in your model provider's dashboard and a counter in your own code. One viral post or one prompt-injection retry loop should not produce a surprise bill in the hundreds or thousands overnight.

Also sanity-check the obvious: prompts don't leak system instructions, user input is treated as untrusted, and you're not logging full prompts with PII in plaintext. This guardrail layer is core to how we ship at SpeedMVPs — it's baked into AI model integration and our AI MVP development work, not bolted on after.

4. Error monitoring you'll actually see

If something breaks at 2am, you need to know without a user emailing you. Before launch:

• Wire up error tracking (Sentry or equivalent) on both frontend and backend.
• Confirm you get a notification — Slack, email, anything — when an unhandled error fires. Trigger a test error and watch it land.
• Add a single uptime ping on your core route.

The bar for an MVP is low but non-zero: you find out before your users do.

5. A rollback path you have actually tested

The most-skipped gate of all. Ask yourself the uncomfortable question: if this deploy is broken, how do I get back to the last good version, and how long does it take?

• On Vercel, Netlify, or similar, confirm you can promote a previous deployment in one click — and do it once as a drill.
• If you ran a database migration, know whether it's reversible. Take a backup before launch regardless.
• Keep a "kill switch": an env var or feature flag that disables the AI feature without a redeploy, in case costs or quality go sideways.

A rollback you've never run is not a rollback. Rehearse it once.

6. The legal and trust minimum

You don't need a law firm for an MVP, but you do need the baseline so you're not exposed and so payment processors don't flag you:

• Privacy policy and terms of service pages that exist and are linked in the footer.
• Cookie/consent banner if you serve EU users or run analytics.
• A clear statement that AI-generated output may be inaccurate, if your product gives advice (medical, legal, financial — be especially careful).
• A working contact method.

The 10-minute final smoke test

Right before you flip the switch, do one cold run as if you're a new user who's never seen the product:

1. Open the live URL in a fresh incognito window on your phone.
2. Sign up with a new email.
3. Complete the single core action your MVP exists to do.
4. Trigger the AI feature and confirm a real, useful response.
5. Make a test payment (or join the waitlist).
6. Force one error and confirm it shows up in your monitoring.
7. Open it on slow mobile data — does it load in a few seconds, or hang?

If all seven pass, you're green. If one fails, that's your blocker — fix it, don't rationalize it.

What is NOT a launch gate (stop polishing)

Equally important: knowing what doesn't block launch. These do not belong on a pre-ship checklist:

• A second or third feature. Ship the one core action.
• Pixel-perfect design on every breakpoint. Mobile-readable is enough.
• Scaling for 100k users. You don't have them yet.
• Admin dashboards, SSO, and integrations no one has asked for.

Founders who can't ship usually fail here — they keep adding instead of gating. If that's you, read why startups fail to ship fast and our take on how to ship an MVP fast. The goal is a launch you can survive and learn from, not a launch with nothing left to learn.

How do I know my MVP is ready to ship?

Your MVP is ready when a brand-new user can sign up, complete your core action, and you'd see it in your dashboard if it broke — all without you touching anything. Run the smoke test above in incognito on your phone with a real test payment. If that round trip works and you've rehearsed one rollback, ship today.

If you're waiting to feel "100% ready," you're already late. Readiness is about gates, not feelings — six green lights, not a calm gut. The point of an MVP is to start learning from real users, and every extra day of polishing is a day of learning you don't get back.

Building toward your first AI launch and want a partner who treats these gates as non-negotiable — typically shipping a production-ready AI MVP from ~$8,000 in 2-3 weeks? Talk to us and we'll pressure-test your readiness before you hit deploy.